人工智能生成数字教育资源适应性评价指标体系构建
Construction of Adaptability Evaluation Indicator System for Artificial Intelligence Generated Digital Educational Resources
罗江华 1岳彦龙1
作者信息
摘要
随着生成式人工智能(GAI)技术的发展,人工智能生成内容(AIGC)成为继用户生成内容(UGC)和专业生成内容(PGC)之后的主流内容生成模式,使得人工智能生成数字教育资源(AIGDER)成为新形态.但是,基于文献综述发现,对AIGDER急需开展评价以提高其适应性,且鲜有对其评价的研究.首先,通过文献综述和适应性及理论分析构建了包含内容质量的可信度、学习过程的支持度、资源规范的符合度以及师生使用的满意度四个要素的AIGDER适应性评价模型.其次,基于该评价模型构建了初始评价指标体系,并利用德尔菲法进行了修订,最终形成包含4个一级指标和19个二级指标的AIGDER适应性评价指标体系;利用层次分析法为该指标体系赋予权重并进行了合理性分析.最后,对该评价指标体系进行了小范围试用,验证了其科学性和有效性,表明该评价指标体系可以作为评价AIGDER适应性的可参考评价工具.
Abstract
With the development of Generative Artificial Intelligence(GAI)technology,Artificial Intelligence Generated Content(AIGC)has become a mainstream content generation model after User-Generated Content(UGC)and Professional-Generated Content(PGC),leading to the emergence of Artificial Intelligence Generated Digital Education Resources(AIGDER).However,based on litera-ture review,it is urgent to evaluate AIGDER to improve its adaptability,but there is limited research on this topic.Firstly,an AIGDER a-daptability evaluation model consisting of four elements:credibility of content quality,support for the learning process,conformity with re-source specifications,and satisfaction of teachers and students was constructed through literature review and theoretical analysis of adaptabil-ity.Secondly,an initial evaluation index system was built based on the evaluation model,and revised using the Delphi method,resulting in an AIGDER adaptability evaluation index system consisting of four primary indicators and nineteen secondary indicators.The weighting of this index system was assigned using the analytic hierarchy process(AHP)and a rationality analysis was conducted.Finally,the evaluation index system was tested in a small-scale trial,which verified its scientificity and effectiveness.This indicates that the evaluation index sys-tem can serve as a reference tool for evaluating the adaptability of AIGDER.
关键词
GAI/AIGC/人工智能生成数字教育资源/AIGDER/适应性评价/指标体系Key words
GAI/AIGC/Artificial Intelligence Generated Digital Educational Resources/AIGDER/Adaptability Evaluation/Indica-tor System引用本文复制引用
基金项目
国家社会科学基金教育学重点项目(2021)(ACA210010)
出版年
2024
NETL
NSTL
万方数据